This invention is in the field of oil and gas production, and is more specifically directed to production optimization from large production fields.
As is well known in the art, modern large oil and gas production fields may include hundreds of wells and a complex network of surface-deployed pipelines ("surface lines") that interconnect these wells with centralized processing facilities. Conventional central processing facilities include such equipment as separators, gas compressors, and the like. While the primary production phase of operation may be effected in a relatively straightforward manner, later life operation of the production field requires important decisions for optimum production and economic return.
The decisions involved in later life production operation includes so-called secondary processes. Typically, secondary production processes are used to increase and continue production from modern production fields. These secondary processes may include waterflooding of the field, gas lift operations applied to individual wells, and also the injection of gas into the formation. Secondary production operations are typically ongoing operations that may be managed on as frequently as a daily basis.
Gas lift is a secondary recovery technique in which gas is injected into an annulus surrounding the production tubing in a producing well. As is fundamental in the art, incremental fluid flow from a well is approximately proportional to the difference in pressure between the reservoir pressure and the pressure in the production tubing at the reservoir depth (generally referred to as P.sub.wf). The pressure P.sub.wf may be generally considered as the sum of the production header pressure at the wellhead plus the combination of the static head within the well and the frictional losses therein. The injection of lift gas into the tubing string reduces the static head from the wellhead to the reservoir depth, and thus increases the pressure differential between the reservoir pressure and the pressure in the tubing at the reservoir depth. The lift gas also tends to increase the frictional pressure loss in the production tubing, especially at high gas-liquid ratios. Considering that the lift gas becomes part of the outflow from the well, a particular lift gas flow rate generally exists for each well at which oil production is maximized.
Production field operation is often constrained by the capacity of the centralized processing facilities. For example, the capacity of centralized gas compression facilities to compress gas for gas lift operations is a critical factor in the operation of the field, as the maximizing of oil production, at individual wells, can produce more gas than can be handled by the compression facilities, resulting in backpressure on producing wells which suppresses oil production. In "gas-mature" regions of the reservoir, over-injection of gas (through gas injection, rather than gas lift) can cause gas to recycle from injector sites to producing wells, thus increasing the formation gas-oil ratio (FGOR) (generally defined as the gas-oil ratio, of a non-gas-lifted well, for the next incremental volume produced) at the producing wells and further exacerbating the excess gas problem. Furthermore, as a well becomes more mature, the effectiveness of gas lift in increasing oil production decreases (i.e., as evidenced by a higher incremental gas-liquid ratio, or IGOR, which is a measure of the efficiency with which additional oil may be produced with the incremental addition of lift gas); as a result, little or no gas is needed for such high gas-recycle wells. The limitation of gas compression capacity is especially problematic for those remote production fields from which produced natural gas is not brought to market, but instead is used only as local fuel, for gas lift injection, for immiscible or miscible injection for reservoir pressure support, or simply to occupy storage areas of the reservoir.
These operations are made significantly more complex by variations in well maturity over a large number of wells in the production field in combination with limited gas handling capacity. If gas handling capacity were effectively infinite, all wells could be in production, regardless of their FGOR and IGOR, and gas-lifted wells could be operated at maximum output rate. However, gas compression capacity limitations require the amount of produced gas (i.e., the sum of formation and lift gas) to be controlled, which in turn limits the extent to which gas lift operations may be carried out, such that gas lift is applied only to those wells having low IGORs. Because well maturity varies from well-to-well over the production field, these production management decisions must be made on substantially a well-by-well basis.
One conventional approach to production field management is to determine FGOR and IGOR cutoffs. In this approach, wells having a less favorable IGOR than the IGOR cutoff value do not receive gas lift, and non-gas-lifted wells having a less favorable FGOR than the FGOR cutoff value are simply shut-in; this approach is intended to maximize oil production while keeping gas output at or below the compression capacity. Considering that a large number of wells are present in a modern field, however, it is difficult to optimize the FGOR and IGOR cutoff values for such fields. Besides changes in the gas-oil ratios (FGOR and IGOR), the water "load" produced by each well also varies with well maturity and may have to be considered in the production decisions, further complicating the optimization process.
As noted above, the wells in a modern production field are linked together by a network of surface lines which transport the produced gas, oil, and water from each well to the centralized processing facilities. The capacity and pressure within each of the surface lines is an important factor in the production from its associated wells, because, as noted above, it is the pressure differential between the reservoir pressure and the wellhead pressure (plus the static head and frictional losses in the well) that determines the volume of production from the well. In large production fields, however, the wellhead pressure is determined by the oil, water, and gas loads from other wells at the same drill site, from other drill sites, and also the capacity of the central processing facilities, relative to the field production. This complex interaction of the wells with one another, and also with the central processing facilities, still further complicates the optimization of field operations.
Heretofore, petroleum production optimization has generally focused on sequential univariate optimization of variables within individual wells. One type of conventional modeling is applied to individual wells to determine output flow rates therefrom, and is referred to in the art as "nodal analysis". An example of nodal analysis is provided in Mach, "Apply nodal analysis to production systems," Well Servicing (January/February 1981), pp. 38-45. As described therein, nodal analysis models each producing well by analyzing each downhole and surface point at which a production pressure drop is present. Each pressure drop point is classified as a node, and a pressure or flow rate response function is assigned thereto. Solution of the system of equations, for example by way of a graphical technique, provides a solution function for the output from the well as a function of one of the pressures, such as reservoir pressure, wellhead pressure, or pressure at the bottom of the well hole (P.sub.wf). While nodal analysis provides a rigorous model for individual wells, interaction among multiple wells in the production field is not considered by this approach.
Another type of production modeling involves multivariate gas handling optimization over the field. Examples of these optimization approaches are described in Kleyweg, et al., "Gaslift Optimization--Claymore Field", 58.sup.th Annual Technical Conference and Exhibition of the Society of Petroleum Engineers (1983), Paper SPE 11885; Nishikiori, et al., "An Improved Method for Gas Lift Allocation Optimization", 64.sup.th Annual Technical Conference and Exhibition of the Society of Petroleum Engineers (1989), Paper SPE 19711.
Surface line hydraulics models are also well known in the art. For example, the approach described in Beggs and Brill, "A Study of Two Phase Flow in Inclined Pipes", Trans. AIME (1973), pp. 607 et seq., models the pressure drops in surface line networks as a function of flow rates of multiple phases (gas, oil, and water). This, and other, rigorous surface line hydraulic models generally require a great deal of computational resources, even on modern high-performance computers.
Rigorous models of individual well performance, lift gas effect, and surface line hydraulics, have been used in the optimization of production field operations. However, the optimization provided by such models relies upon the determination of derivatives of each of the underlying model functions. In actual field operations, however, the activities carried out by field operators to functionally operate the field are "discontinuous" functions, such as shutting in wells, and initiating lift gas operations. The "discontinuities" resulting from these actual field operations are not compatible with optimization through the application of rigorous, derivative-dependent, models, because derivative-based models are vulnerable to convergence upon local extrema in the modeling of discontinuous events, resulting in inaccurate and poor optimization results. In addition, the optimization of the operation of modern complex production fields, having a large number of individual wells that interact with one another, requires inordinate amounts of computational resources when performed using conventional rigorous models.
By way of further background, the optimization of a modern production field through use of an adaptive network, or "neural network", is described in Stoisits, et al., "Gas Optimization at the Kuparuk River Field", 69.sup.th Annual Technical Conference and Exhibition of the Society of Petroleum Engineers (1994), Paper SPE 28467, pp. 35-42. As described in this reference, IGOR and FGOR cutoff values are optimized by way of an iterative evaluation process applied to each one of multiple central processing facilities (CPFs), with the produced gas checked against the compression capacity of the CPF. Re-routing of production among the multiple CPFs is also considered in this approach, as wells associated with one CPF that may be shut-in or not gas-lifted may be more productive than producing wells associated with a different (i.e., less loaded) CPF. The surface line hydraulic effects of re-routing production among CPFs are quite complex, however; as such, an adaptive network is used in this approach to replace a conventional surface line hydraulic simulation model, in optimizing the drill site oil, gas, and water rates, and separator pressure, as measured by drill site pressure. The adaptive network described in this reference was trained by way of the well-known back propagation method by operating the network to calculate surface pressure line drops over a wide range of oil, water, and gas flow rates; comparison of the adaptive network results were used in back propagation regression to set the network weighting factors.
By way of further background, optimization methods known as "genetic algorithms" are known in the art. Conventional genetic algorithms serve to select a string (referred to as a "solution vector", or "chromosome"), consisting of digits ("genes") having values ("alleles") that provide the optimum value when applied to a "fitness function" modeling the desired optimization situation. According to this technique, a group, or "generation", of chromosomes is randomly generated, and the fitness function is evaluated for each chromosome. A successor generation is then produced from the previous generation, with selection made according to the evaluated fitness function; for example, a probability function may assign a probability value to each of the chromosomes in the generation according to its fitness function value. In any case, a chromosome that produced a higher fitness function value is more likely to be selected for use in producing the next generation than a chromosome that produced a lower fitness function value. A "reproduction pool" of chromosomes is then produced by random selection of the first generation of chromosomes, with the random selection weighted according to the fitness function results. Pairs of chromosomes are then randomly selected, from the reproduction pool, for "reproduction" with one another by the exchanging of "genes" on either side of a "crossover" point within the chromosomes; the reproduction then produces a second generation of chromosomes for evaluation of the fitness function. Mutation may be introduced through the random alteration of a small fraction (e.g., 1/1000) of the genes in each generation. Iterative evaluation and reproduction of the chromosomes in this manner eventually converges upon an optimized chromosome.